Overview

Dataset statistics

Number of variables17
Number of observations3306
Missing cells6176
Missing cells (%)11.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory439.2 KiB
Average record size in memory136.0 B

Variable types

Categorical5
Numeric12

Alerts

year_dt has constant value "1970"Constant
Country Name has a high cardinality: 174 distinct valuesHigh cardinality
Country Code has a high cardinality: 174 distinct valuesHigh cardinality
Life Expectancy World Bank is highly overall correlated with Prevelance of Undernourishment and 4 other fieldsHigh correlation
Prevelance of Undernourishment is highly overall correlated with Life Expectancy World Bank and 3 other fieldsHigh correlation
CO2 is highly overall correlated with Life Expectancy World Bank and 3 other fieldsHigh correlation
Sanitation is highly overall correlated with Life Expectancy World Bank and 2 other fieldsHigh correlation
Injuries is highly overall correlated with CO2 and 2 other fieldsHigh correlation
Communicable is highly overall correlated with Life Expectancy World Bank and 3 other fieldsHigh correlation
NonCommunicable is highly overall correlated with CO2 and 2 other fieldsHigh correlation
Region is highly overall correlated with IncomeGroupHigh correlation
IncomeGroup is highly overall correlated with Life Expectancy World Bank and 2 other fieldsHigh correlation
Life Expectancy World Bank has 188 (5.7%) missing valuesMissing
Prevelance of Undernourishment has 684 (20.7%) missing valuesMissing
CO2 has 152 (4.6%) missing valuesMissing
Health Expenditure % has 180 (5.4%) missing valuesMissing
Education Expenditure % has 1090 (33.0%) missing valuesMissing
Unemployment has 304 (9.2%) missing valuesMissing
Corruption has 2331 (70.5%) missing valuesMissing
Sanitation has 1247 (37.7%) missing valuesMissing
Country Name is uniformly distributedUniform
Country Code is uniformly distributedUniform
Injuries has unique valuesUnique
Communicable has unique valuesUnique
NonCommunicable has unique valuesUnique

Reproduction

Analysis started2023-10-02 23:52:20.094386
Analysis finished2023-10-02 23:52:35.545480
Duration15.45 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct174
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Afghanistan
 
19
Nepal
 
19
Malawi
 
19
Malaysia
 
19
Namibia
 
19
Other values (169)
3211 

Length

Max length24
Median length21
Mean length8.3275862
Min length4

Characters and Unicode

Total characters27531
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAngola
3rd rowAlbania
4th rowAndorra
5th rowUnited_Arab_Emirates

Common Values

ValueCountFrequency (%)
Afghanistan 19
 
0.6%
Nepal 19
 
0.6%
Malawi 19
 
0.6%
Malaysia 19
 
0.6%
Namibia 19
 
0.6%
Niger 19
 
0.6%
Nigeria 19
 
0.6%
Nicaragua 19
 
0.6%
Netherlands 19
 
0.6%
Norway 19
 
0.6%
Other values (164) 3116
94.3%

Length

2023-10-03T02:52:35.621998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afghanistan 19
 
0.6%
australia 19
 
0.6%
austria 19
 
0.6%
albania 19
 
0.6%
andorra 19
 
0.6%
united_arab_emirates 19
 
0.6%
argentina 19
 
0.6%
armenia 19
 
0.6%
american_samoa 19
 
0.6%
antigua_and_barbuda 19
 
0.6%
Other values (164) 3116
94.3%

Most occurring characters

ValueCountFrequency (%)
a 4503
16.4%
i 2432
 
8.8%
n 2242
 
8.1%
e 1729
 
6.3%
r 1634
 
5.9%
o 1501
 
5.5%
u 1140
 
4.1%
l 1026
 
3.7%
t 969
 
3.5%
d 931
 
3.4%
Other values (42) 9424
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22838
83.0%
Uppercase Letter 3952
 
14.4%
Connector Punctuation 703
 
2.6%
Other Punctuation 19
 
0.1%
Dash Punctuation 19
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4503
19.7%
i 2432
10.6%
n 2242
9.8%
e 1729
 
7.6%
r 1634
 
7.2%
o 1501
 
6.6%
u 1140
 
5.0%
l 1026
 
4.5%
t 969
 
4.2%
d 931
 
4.1%
Other values (16) 4731
20.7%
Uppercase Letter
ValueCountFrequency (%)
S 456
11.5%
M 380
 
9.6%
B 342
 
8.7%
A 304
 
7.7%
G 266
 
6.7%
C 266
 
6.7%
N 228
 
5.8%
T 209
 
5.3%
I 209
 
5.3%
P 209
 
5.3%
Other values (13) 1083
27.4%
Connector Punctuation
ValueCountFrequency (%)
_ 703
100.0%
Other Punctuation
ValueCountFrequency (%)
' 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26790
97.3%
Common 741
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4503
16.8%
i 2432
 
9.1%
n 2242
 
8.4%
e 1729
 
6.5%
r 1634
 
6.1%
o 1501
 
5.6%
u 1140
 
4.3%
l 1026
 
3.8%
t 969
 
3.6%
d 931
 
3.5%
Other values (39) 8683
32.4%
Common
ValueCountFrequency (%)
_ 703
94.9%
' 19
 
2.6%
- 19
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4503
16.4%
i 2432
 
8.8%
n 2242
 
8.1%
e 1729
 
6.3%
r 1634
 
5.9%
o 1501
 
5.5%
u 1140
 
4.1%
l 1026
 
3.7%
t 969
 
3.5%
d 931
 
3.4%
Other values (42) 9424
34.2%

Country Code
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct174
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
AFG
 
19
NPL
 
19
MWI
 
19
MYS
 
19
NAM
 
19
Other values (169)
3211 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9918
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAGO
3rd rowALB
4th rowAND
5th rowARE

Common Values

ValueCountFrequency (%)
AFG 19
 
0.6%
NPL 19
 
0.6%
MWI 19
 
0.6%
MYS 19
 
0.6%
NAM 19
 
0.6%
NER 19
 
0.6%
NGA 19
 
0.6%
NIC 19
 
0.6%
NLD 19
 
0.6%
NOR 19
 
0.6%
Other values (164) 3116
94.3%

Length

2023-10-03T02:52:35.715804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg 19
 
0.6%
aus 19
 
0.6%
aut 19
 
0.6%
alb 19
 
0.6%
and 19
 
0.6%
are 19
 
0.6%
arg 19
 
0.6%
arm 19
 
0.6%
asm 19
 
0.6%
atg 19
 
0.6%
Other values (164) 3116
94.3%

Most occurring characters

ValueCountFrequency (%)
A 779
 
7.9%
R 760
 
7.7%
M 741
 
7.5%
N 722
 
7.3%
L 570
 
5.7%
B 551
 
5.6%
S 551
 
5.6%
T 513
 
5.2%
G 494
 
5.0%
U 456
 
4.6%
Other values (16) 3781
38.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9918
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 779
 
7.9%
R 760
 
7.7%
M 741
 
7.5%
N 722
 
7.3%
L 570
 
5.7%
B 551
 
5.6%
S 551
 
5.6%
T 513
 
5.2%
G 494
 
5.0%
U 456
 
4.6%
Other values (16) 3781
38.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9918
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 779
 
7.9%
R 760
 
7.7%
M 741
 
7.5%
N 722
 
7.3%
L 570
 
5.7%
B 551
 
5.6%
S 551
 
5.6%
T 513
 
5.2%
G 494
 
5.0%
U 456
 
4.6%
Other values (16) 3781
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 779
 
7.9%
R 760
 
7.7%
M 741
 
7.5%
N 722
 
7.3%
L 570
 
5.7%
B 551
 
5.6%
S 551
 
5.6%
T 513
 
5.2%
G 494
 
5.0%
U 456
 
4.6%
Other values (16) 3781
38.1%

Region
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
Europe_&_Central_Asia
893 
Sub-Saharan_Africa
836 
Latin_America_&_Caribbean
551 
East_Asia_&_Pacific
513 
Middle_East_&_North_Africa
304 
Other values (2)
209 

Length

Max length26
Median length25
Mean length20.413793
Min length10

Characters and Unicode

Total characters67488
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth_Asia
2nd rowSub-Saharan_Africa
3rd rowEurope_&_Central_Asia
4th rowEurope_&_Central_Asia
5th rowMiddle_East_&_North_Africa

Common Values

ValueCountFrequency (%)
Europe_&_Central_Asia 893
27.0%
Sub-Saharan_Africa 836
25.3%
Latin_America_&_Caribbean 551
16.7%
East_Asia_&_Pacific 513
15.5%
Middle_East_&_North_Africa 304
 
9.2%
South_Asia 152
 
4.6%
North_America 57
 
1.7%

Length

2023-10-03T02:52:35.804015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-03T02:52:35.916096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
europe_&_central_asia 893
27.0%
sub-saharan_africa 836
25.3%
latin_america_&_caribbean 551
16.7%
east_asia_&_pacific 513
15.5%
middle_east_&_north_africa 304
 
9.2%
south_asia 152
 
4.6%
north_america 57
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 9690
14.4%
_ 8132
 
12.0%
i 5738
 
8.5%
r 5282
 
7.8%
A 3306
 
4.9%
e 3249
 
4.8%
n 2831
 
4.2%
c 2774
 
4.1%
t 2774
 
4.1%
s 2375
 
3.5%
Other values (18) 21337
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46246
68.5%
Uppercase Letter 10013
 
14.8%
Connector Punctuation 8132
 
12.0%
Other Punctuation 2261
 
3.4%
Dash Punctuation 836
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9690
21.0%
i 5738
12.4%
r 5282
11.4%
e 3249
 
7.0%
n 2831
 
6.1%
c 2774
 
6.0%
t 2774
 
6.0%
s 2375
 
5.1%
b 1938
 
4.2%
u 1881
 
4.1%
Other values (7) 7714
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 3306
33.0%
S 1824
18.2%
E 1710
17.1%
C 1444
14.4%
L 551
 
5.5%
P 513
 
5.1%
N 361
 
3.6%
M 304
 
3.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8132
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2261
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56259
83.4%
Common 11229
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9690
17.2%
i 5738
 
10.2%
r 5282
 
9.4%
A 3306
 
5.9%
e 3249
 
5.8%
n 2831
 
5.0%
c 2774
 
4.9%
t 2774
 
4.9%
s 2375
 
4.2%
b 1938
 
3.4%
Other values (15) 16302
29.0%
Common
ValueCountFrequency (%)
_ 8132
72.4%
& 2261
 
20.1%
- 836
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9690
14.4%
_ 8132
 
12.0%
i 5738
 
8.5%
r 5282
 
7.8%
A 3306
 
4.9%
e 3249
 
4.8%
n 2831
 
4.2%
c 2774
 
4.1%
t 2774
 
4.1%
s 2375
 
3.5%
Other values (18) 21337
31.6%

IncomeGroup
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
High_income
1083 
Upper_middle_income
931 
Lower_middle_income
855 
Low_income
437 

Length

Max length19
Median length19
Mean length15.189655
Min length10

Characters and Unicode

Total characters50217
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow_income
2nd rowLower_middle_income
3rd rowUpper_middle_income
4th rowHigh_income
5th rowHigh_income

Common Values

ValueCountFrequency (%)
High_income 1083
32.8%
Upper_middle_income 931
28.2%
Lower_middle_income 855
25.9%
Low_income 437
13.2%

Length

2023-10-03T02:52:36.019826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-03T02:52:36.108459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
high_income 1083
32.8%
upper_middle_income 931
28.2%
lower_middle_income 855
25.9%
low_income 437
13.2%

Most occurring characters

ValueCountFrequency (%)
e 6878
13.7%
i 6175
12.3%
m 5092
10.1%
_ 5092
10.1%
o 4598
9.2%
d 3572
7.1%
n 3306
 
6.6%
c 3306
 
6.6%
p 1862
 
3.7%
r 1786
 
3.6%
Other values (7) 8550
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41819
83.3%
Connector Punctuation 5092
 
10.1%
Uppercase Letter 3306
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6878
16.4%
i 6175
14.8%
m 5092
12.2%
o 4598
11.0%
d 3572
8.5%
n 3306
7.9%
c 3306
7.9%
p 1862
 
4.5%
r 1786
 
4.3%
l 1786
 
4.3%
Other values (3) 3458
8.3%
Uppercase Letter
ValueCountFrequency (%)
L 1292
39.1%
H 1083
32.8%
U 931
28.2%
Connector Punctuation
ValueCountFrequency (%)
_ 5092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45125
89.9%
Common 5092
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6878
15.2%
i 6175
13.7%
m 5092
11.3%
o 4598
10.2%
d 3572
7.9%
n 3306
7.3%
c 3306
7.3%
p 1862
 
4.1%
r 1786
 
4.0%
l 1786
 
4.0%
Other values (6) 6764
15.0%
Common
ValueCountFrequency (%)
_ 5092
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6878
13.7%
i 6175
12.3%
m 5092
10.1%
_ 5092
10.1%
o 4598
9.2%
d 3572
7.1%
n 3306
 
6.6%
c 3306
 
6.6%
p 1862
 
3.7%
r 1786
 
3.6%
Other values (7) 8550
17.0%

Year
Real number (ℝ)

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum2001
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:36.193336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12005
median2010
Q32015
95-th percentile2019
Maximum2019
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4780541
Coefficient of variation (CV)0.0027254001
Kurtosis-1.2066765
Mean2010
Median Absolute Deviation (MAD)5
Skewness0
Sum6645060
Variance30.009077
MonotonicityIncreasing
2023-10-03T02:52:36.275588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2001 174
 
5.3%
2011 174
 
5.3%
2018 174
 
5.3%
2017 174
 
5.3%
2016 174
 
5.3%
2015 174
 
5.3%
2014 174
 
5.3%
2013 174
 
5.3%
2012 174
 
5.3%
2010 174
 
5.3%
Other values (9) 1566
47.4%
ValueCountFrequency (%)
2001 174
5.3%
2002 174
5.3%
2003 174
5.3%
2004 174
5.3%
2005 174
5.3%
2006 174
5.3%
2007 174
5.3%
2008 174
5.3%
2009 174
5.3%
2010 174
5.3%
ValueCountFrequency (%)
2019 174
5.3%
2018 174
5.3%
2017 174
5.3%
2016 174
5.3%
2015 174
5.3%
2014 174
5.3%
2013 174
5.3%
2012 174
5.3%
2011 174
5.3%
2010 174
5.3%

Life Expectancy World Bank
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2966
Distinct (%)95.1%
Missing188
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean69.748362
Minimum40.369
Maximum84.356341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:36.378740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum40.369
5-th percentile51.441
Q163.642
median72.1685
Q376.809
95-th percentile81.64922
Maximum84.356341
Range43.987341
Interquartile range (IQR)13.167

Descriptive statistics

Standard deviation9.4081541
Coefficient of variation (CV)0.1348871
Kurtosis-0.20295242
Mean69.748362
Median Absolute Deviation (MAD)5.9985
Skewness-0.77388676
Sum217475.39
Variance88.513364
MonotonicityNot monotonic
2023-10-03T02:52:36.486634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.70243902 4
 
0.1%
82.20487805 3
 
0.1%
79.43902439 3
 
0.1%
80.74634146 3
 
0.1%
81.9 3
 
0.1%
75.4 3
 
0.1%
71.249 3
 
0.1%
75.376 3
 
0.1%
78.53902439 3
 
0.1%
74.904 3
 
0.1%
Other values (2956) 3087
93.4%
(Missing) 188
 
5.7%
ValueCountFrequency (%)
40.369 1
< 0.1%
41.376 1
< 0.1%
42.419 1
< 0.1%
42.518 1
< 0.1%
42.595 1
< 0.1%
42.658 1
< 0.1%
42.731 1
< 0.1%
42.733 1
< 0.1%
42.854 1
< 0.1%
43.065 1
< 0.1%
ValueCountFrequency (%)
84.35634146 1
< 0.1%
84.21097561 1
< 0.1%
84.0997561 1
< 0.1%
83.98487805 1
< 0.1%
83.90487805 1
< 0.1%
83.83170732 1
< 0.1%
83.79390244 1
< 0.1%
83.75365854 1
< 0.1%
83.60243902 1
< 0.1%
83.59512195 1
< 0.1%

Prevelance of Undernourishment
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct399
Distinct (%)15.2%
Missing684
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean10.663654
Minimum2.5
Maximum70.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:36.597451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.5
Q12.5
median6.2
Q314.775
95-th percentile34.7
Maximum70.9
Range68.4
Interquartile range (IQR)12.275

Descriptive statistics

Standard deviation11.285897
Coefficient of variation (CV)1.0583518
Kurtosis5.4575976
Mean10.663654
Median Absolute Deviation (MAD)3.7
Skewness2.1176294
Sum27960.1
Variance127.37147
MonotonicityNot monotonic
2023-10-03T02:52:36.703884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 796
24.1%
3.7 27
 
0.8%
3.4 22
 
0.7%
3.2 19
 
0.6%
5 19
 
0.6%
3.1 19
 
0.6%
4.3 19
 
0.6%
8.8 18
 
0.5%
4.4 18
 
0.5%
7.1 17
 
0.5%
Other values (389) 1648
49.8%
(Missing) 684
20.7%
ValueCountFrequency (%)
2.5 796
24.1%
2.6 14
 
0.4%
2.7 8
 
0.2%
2.8 16
 
0.5%
2.9 7
 
0.2%
3 13
 
0.4%
3.1 19
 
0.6%
3.2 19
 
0.6%
3.3 15
 
0.5%
3.4 22
 
0.7%
ValueCountFrequency (%)
70.9 3
0.1%
70.8 2
0.1%
70.7 1
 
< 0.1%
70.6 2
0.1%
70.5 1
 
< 0.1%
70.4 1
 
< 0.1%
68.7 1
 
< 0.1%
67.5 1
 
< 0.1%
65.9 1
 
< 0.1%
63.2 1
 
< 0.1%

CO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2259
Distinct (%)71.6%
Missing152
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean157492.41
Minimum9.9999998
Maximum10707220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:36.818805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.9999998
5-th percentile170
Q12002.5
median10205
Q358772.499
95-th percentile459432.99
Maximum10707220
Range10707210
Interquartile range (IQR)56769.999

Descriptive statistics

Standard deviation772641.53
Coefficient of variation (CV)4.9058969
Kurtosis99.754265
Mean157492.41
Median Absolute Deviation (MAD)9904.9999
Skewness9.4378155
Sum4.9673107 × 108
Variance5.9697494 × 1011
MonotonicityNot monotonic
2023-10-03T02:52:36.932753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000072 18
 
0.5%
9.999999776 18
 
0.5%
170.0000018 15
 
0.5%
140.0000006 15
 
0.5%
250 13
 
0.4%
119.9999973 12
 
0.4%
70.0000003 12
 
0.4%
59.99999866 12
 
0.4%
209.9999934 11
 
0.3%
129.9999952 11
 
0.3%
Other values (2249) 3017
91.3%
(Missing) 152
 
4.6%
ValueCountFrequency (%)
9.999999776 18
0.5%
10 1
 
< 0.1%
30 1
 
< 0.1%
39.99999911 8
0.2%
50 1
 
< 0.1%
50.00000075 6
 
0.2%
59.99999866 12
0.4%
70.0000003 12
0.4%
79.99999821 5
 
0.2%
80 1
 
< 0.1%
ValueCountFrequency (%)
10707219.73 1
< 0.1%
10502929.69 1
< 0.1%
10096009.77 1
< 0.1%
10006669.92 1
< 0.1%
9984570.312 1
< 0.1%
9874660.156 1
< 0.1%
9861099.609 1
< 0.1%
9541870.117 1
< 0.1%
9282549.805 1
< 0.1%
8474919.922 1
< 0.1%

Health Expenditure %
Real number (ℝ)

Distinct3126
Distinct (%)100.0%
Missing180
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean6.3640591
Minimum1.263576
Maximum24.23068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:37.043841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.263576
5-th percentile2.6505123
Q14.2054429
median5.8923523
Q38.1191659
95-th percentile10.831115
Maximum24.23068
Range22.967104
Interquartile range (IQR)3.913723

Descriptive statistics

Standard deviation2.8428437
Coefficient of variation (CV)0.44670291
Kurtosis2.9480836
Mean6.3640591
Median Absolute Deviation (MAD)1.9330953
Skewness1.1574056
Sum19894.049
Variance8.0817604
MonotonicityNot monotonic
2023-10-03T02:52:37.151506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.38575172 1
 
< 0.1%
3.42069316 1
 
< 0.1%
7.6386919 1
 
< 0.1%
10.5835619 1
 
< 0.1%
8.92953396 1
 
< 0.1%
4.63011789 1
 
< 0.1%
9.4396801 1
 
< 0.1%
9.3601923 1
 
< 0.1%
2.79134297 1
 
< 0.1%
2.6028018 1
 
< 0.1%
Other values (3116) 3116
94.3%
(Missing) 180
 
5.4%
ValueCountFrequency (%)
1.26357603 1
< 0.1%
1.49993956 1
< 0.1%
1.51855373 1
< 0.1%
1.5251174 1
< 0.1%
1.55349779 1
< 0.1%
1.57114804 1
< 0.1%
1.59628761 1
< 0.1%
1.59996212 1
< 0.1%
1.62753129 1
< 0.1%
1.63775349 1
< 0.1%
ValueCountFrequency (%)
24.23068047 1
< 0.1%
23.96181297 1
< 0.1%
23.0041008 1
< 0.1%
22.0241642 1
< 0.1%
21.95516396 1
< 0.1%
20.413414 1
< 0.1%
19.72741699 1
< 0.1%
19.18874931 1
< 0.1%
19.03600502 1
< 0.1%
18.60474205 1
< 0.1%

Education Expenditure %
Real number (ℝ)

Distinct2192
Distinct (%)98.9%
Missing1090
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean4.5890142
Minimum0.85031998
Maximum23.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:37.260323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.85031998
5-th percentile1.9658526
Q13.1361175
median4.371465
Q35.5198251
95-th percentile7.9137701
Maximum23.27
Range22.41968
Interquartile range (IQR)2.3837076

Descriptive statistics

Standard deviation2.1191647
Coefficient of variation (CV)0.46179083
Kurtosis9.3075312
Mean4.5890142
Median Absolute Deviation (MAD)1.1923649
Skewness2.024186
Sum10169.256
Variance4.4908591
MonotonicityNot monotonic
2023-10-03T02:52:37.367034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.299999952 3
 
0.1%
2.099999905 3
 
0.1%
3.75 3
 
0.1%
3.5 3
 
0.1%
3.109630108 2
 
0.1%
3.517210007 2
 
0.1%
3.569999933 2
 
0.1%
4.508979797 2
 
0.1%
2.25999999 2
 
0.1%
3.799999952 2
 
0.1%
Other values (2182) 2192
66.3%
(Missing) 1090
33.0%
ValueCountFrequency (%)
0.8503199816 1
< 0.1%
0.8999999762 1
< 0.1%
0.962890029 1
< 0.1%
1.021950006 1
< 0.1%
1.024500012 1
< 0.1%
1.099720001 1
< 0.1%
1.107920051 1
< 0.1%
1.117609978 1
< 0.1%
1.152660012 1
< 0.1%
1.164250016 1
< 0.1%
ValueCountFrequency (%)
23.27000046 1
< 0.1%
20.59000015 1
< 0.1%
19.70999908 1
< 0.1%
17.62999916 1
< 0.1%
17.55999947 1
< 0.1%
15.98999977 1
< 0.1%
15.75 1
< 0.1%
15.67000008 1
< 0.1%
15.59000015 1
< 0.1%
15.27999973 1
< 0.1%

Unemployment
Real number (ℝ)

Distinct2151
Distinct (%)71.7%
Missing304
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean7.8907605
Minimum0.1
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:37.474262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.1313
Q13.733
median5.9200001
Q310.0975
95-th percentile20.907949
Maximum37.25
Range37.15
Interquartile range (IQR)6.3645003

Descriptive statistics

Standard deviation6.2708319
Coefficient of variation (CV)0.79470564
Kurtosis2.7825099
Mean7.8907605
Median Absolute Deviation (MAD)2.79
Skewness1.6275363
Sum23688.063
Variance39.323332
MonotonicityNot monotonic
2023-10-03T02:52:37.582736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 12
 
0.4%
3.099999905 8
 
0.2%
5 7
 
0.2%
5.650000095 7
 
0.2%
4.800000191 6
 
0.2%
5.380000114 6
 
0.2%
7.21999979 6
 
0.2%
6.809999943 6
 
0.2%
5.300000191 6
 
0.2%
4.900000095 6
 
0.2%
Other values (2141) 2932
88.7%
(Missing) 304
 
9.2%
ValueCountFrequency (%)
0.1000000015 1
< 0.1%
0.1099999994 1
< 0.1%
0.1400000006 2
0.1%
0.1430000067 1
< 0.1%
0.1469999999 1
< 0.1%
0.150000006 1
< 0.1%
0.1700000018 1
< 0.1%
0.200000003 1
< 0.1%
0.25 1
< 0.1%
0.2800000012 1
< 0.1%
ValueCountFrequency (%)
37.25 1
< 0.1%
37.15999985 1
< 0.1%
36.68999863 1
< 0.1%
36.02999878 1
< 0.1%
34.93000031 1
< 0.1%
34.63499832 1
< 0.1%
33.86000061 1
< 0.1%
33.75999832 1
< 0.1%
33.29000092 1
< 0.1%
32.95500183 1
< 0.1%

Corruption
Real number (ℝ)

Distinct8
Distinct (%)0.8%
Missing2331
Missing (%)70.5%
Infinite0
Infinite (%)0.0%
Mean2.8605128
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:37.690085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q33.25
95-th percentile4
Maximum4.5
Range3.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.6213433
Coefficient of variation (CV)0.21721395
Kurtosis0.0076012034
Mean2.8605128
Median Absolute Deviation (MAD)0.5
Skewness-0.070765155
Sum2789
Variance0.3860675
MonotonicityNot monotonic
2023-10-03T02:52:37.792778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 330
 
10.0%
2.5 251
 
7.6%
3.5 176
 
5.3%
2 109
 
3.3%
4 57
 
1.7%
1.5 38
 
1.1%
4.5 11
 
0.3%
1 3
 
0.1%
(Missing) 2331
70.5%
ValueCountFrequency (%)
1 3
 
0.1%
1.5 38
 
1.1%
2 109
 
3.3%
2.5 251
7.6%
3 330
10.0%
3.5 176
5.3%
4 57
 
1.7%
4.5 11
 
0.3%
ValueCountFrequency (%)
4.5 11
 
0.3%
4 57
 
1.7%
3.5 176
5.3%
3 330
10.0%
2.5 251
7.6%
2 109
 
3.3%
1.5 38
 
1.1%
1 3
 
0.1%

Sanitation
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1941
Distinct (%)94.3%
Missing1247
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean52.738785
Minimum2.3776471
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:37.904235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.3776471
5-th percentile8.6790876
Q124.746007
median49.317481
Q380.278847
95-th percentile99.194428
Maximum100
Range97.622357
Interquartile range (IQR)55.532841

Descriptive statistics

Standard deviation30.126762
Coefficient of variation (CV)0.5712449
Kurtosis-1.3550764
Mean52.738785
Median Absolute Deviation (MAD)27.716031
Skewness0.071628871
Sum108589.16
Variance907.62177
MonotonicityNot monotonic
2023-10-03T02:52:38.018206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 57
 
1.7%
91.86797469 19
 
0.6%
70.3715 19
 
0.6%
71.1 8
 
0.2%
32.50916088 6
 
0.2%
74.56568765 5
 
0.2%
58.78889399 4
 
0.1%
97.2 3
 
0.1%
37.28527113 2
 
0.1%
9.425466024 2
 
0.1%
Other values (1931) 1934
58.5%
(Missing) 1247
37.7%
ValueCountFrequency (%)
2.377647105 1
< 0.1%
2.627151311 1
< 0.1%
2.876350728 1
< 0.1%
3.118540241 1
< 0.1%
3.355939326 1
< 0.1%
3.588700651 1
< 0.1%
3.631962583 1
< 0.1%
3.818593969 1
< 0.1%
3.831344237 1
< 0.1%
4.004654451 1
< 0.1%
ValueCountFrequency (%)
100.0000037 1
 
< 0.1%
100.0000035 1
 
< 0.1%
100.0000012 2
 
0.1%
100 57
1.7%
99.99999876 1
 
< 0.1%
99.99999755 1
 
< 0.1%
99.99999528 1
 
< 0.1%
99.99999506 1
 
< 0.1%
99.67939917 1
 
< 0.1%
99.6771794 1
 
< 0.1%

Injuries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1318219.5
Minimum430.49
Maximum55636759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:38.134708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum430.49
5-th percentile2109.4725
Q162456.878
median245690.96
Q3846559.12
95-th percentile3992127.1
Maximum55636759
Range55636329
Interquartile range (IQR)784102.24

Descriptive statistics

Standard deviation5214067.9
Coefficient of variation (CV)3.9553869
Kurtosis74.63591
Mean1318219.5
Median Absolute Deviation (MAD)234577.76
Skewness8.3899316
Sum4.3580335 × 109
Variance2.7186504 × 1013
MonotonicityNot monotonic
2023-10-03T02:52:38.239272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2179727.1 1
 
< 0.1%
859305.23 1
 
< 0.1%
111887.61 1
 
< 0.1%
30900.62 1
 
< 0.1%
405779.44 1
 
< 0.1%
726646.06 1
 
< 0.1%
99407.34 1
 
< 0.1%
670757.77 1
 
< 0.1%
5253874.49 1
 
< 0.1%
169392.04 1
 
< 0.1%
Other values (3296) 3296
99.7%
ValueCountFrequency (%)
430.49 1
< 0.1%
432.67 1
< 0.1%
434.47 1
< 0.1%
437.01 1
< 0.1%
439.9 1
< 0.1%
442.4 1
< 0.1%
443.84 1
< 0.1%
445.84 1
< 0.1%
448.91 1
< 0.1%
452.02 1
< 0.1%
ValueCountFrequency (%)
55636759.3 1
< 0.1%
55112092.7 1
< 0.1%
55078934.61 1
< 0.1%
54648485.34 1
< 0.1%
54420117.23 1
< 0.1%
54362219.04 1
< 0.1%
53914552.02 1
< 0.1%
53868774.94 1
< 0.1%
53697866.83 1
< 0.1%
53563909.73 1
< 0.1%

Communicable
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4686288.6
Minimum330.16
Maximum2.6856461 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:38.350013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum330.16
5-th percentile1815.9275
Q157764.745
median314769.35
Q32831635.9
95-th percentile15779722
Maximum2.6856461 × 108
Range2.6856428 × 108
Interquartile range (IQR)2773871.2

Descriptive statistics

Standard deviation18437269
Coefficient of variation (CV)3.9343008
Kurtosis104.88697
Mean4686288.6
Median Absolute Deviation (MAD)311551.68
Skewness9.4121389
Sum1.549287 × 1010
Variance3.3993289 × 1014
MonotonicityNot monotonic
2023-10-03T02:52:38.464570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9689193.7 1
 
< 0.1%
3538986.78 1
 
< 0.1%
723629.75 1
 
< 0.1%
39509.26 1
 
< 0.1%
5709964.03 1
 
< 0.1%
1166296.26 1
 
< 0.1%
555838.2 1
 
< 0.1%
11370157.59 1
 
< 0.1%
91427866.22 1
 
< 0.1%
248085.86 1
 
< 0.1%
Other values (3296) 3296
99.7%
ValueCountFrequency (%)
330.16 1
< 0.1%
330.39 1
< 0.1%
332.94 1
< 0.1%
333.25 1
< 0.1%
335.86 1
< 0.1%
337.54 1
< 0.1%
338.68 1
< 0.1%
340.39 1
< 0.1%
341.41 1
< 0.1%
343.03 1
< 0.1%
ValueCountFrequency (%)
268564609.8 1
< 0.1%
261766675.1 1
< 0.1%
254933810 1
< 0.1%
249700603.7 1
< 0.1%
245719584.3 1
< 0.1%
239547398.8 1
< 0.1%
233021416.5 1
< 0.1%
226718272.2 1
< 0.1%
217642490.3 1
< 0.1%
210484107.4 1
< 0.1%

NonCommunicable
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3306
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7392488.1
Minimum2481.82
Maximum3.2463781 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 KiB
2023-10-03T02:52:38.577169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2481.82
5-th percentile14592.71
Q1318475.84
median1350146.3
Q33918468.3
95-th percentile23797648
Maximum3.2463781 × 108
Range3.2463533 × 108
Interquartile range (IQR)3599992.4

Descriptive statistics

Standard deviation29326878
Coefficient of variation (CV)3.9671188
Kurtosis71.657394
Mean7392488.1
Median Absolute Deviation (MAD)1263036.4
Skewness8.1757934
Sum2.4439566 × 1010
Variance8.600658 × 1014
MonotonicityNot monotonic
2023-10-03T02:52:38.687960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5795426.38 1
 
< 0.1%
5031592.92 1
 
< 0.1%
478194.75 1
 
< 0.1%
318292.56 1
 
< 0.1%
2242633.92 1
 
< 0.1%
4728365.81 1
 
< 0.1%
338641.31 1
 
< 0.1%
2667354.98 1
 
< 0.1%
28118617.51 1
 
< 0.1%
948464.91 1
 
< 0.1%
Other values (3296) 3296
99.7%
ValueCountFrequency (%)
2481.82 1
< 0.1%
2485.93 1
< 0.1%
2487.82 1
< 0.1%
2498.8 1
< 0.1%
2502.98 1
< 0.1%
2526.98 1
< 0.1%
2575.11 1
< 0.1%
2618 1
< 0.1%
2653.62 1
< 0.1%
2704.27 1
< 0.1%
ValueCountFrequency (%)
324637810.5 1
< 0.1%
319066226.4 1
< 0.1%
314747193.3 1
< 0.1%
310204381.1 1
< 0.1%
304811336.4 1
< 0.1%
301933553.1 1
< 0.1%
299286678.6 1
< 0.1%
297330095.4 1
< 0.1%
295761448.8 1
< 0.1%
293019139 1
< 0.1%

year_dt
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
1970
3306 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters13224
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1970
2nd row1970
3rd row1970
4th row1970
5th row1970

Common Values

ValueCountFrequency (%)
1970 3306
100.0%

Length

2023-10-03T02:52:38.790250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-03T02:52:38.867669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1970 3306
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3306
25.0%
9 3306
25.0%
7 3306
25.0%
0 3306
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13224
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3306
25.0%
9 3306
25.0%
7 3306
25.0%
0 3306
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13224
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3306
25.0%
9 3306
25.0%
7 3306
25.0%
0 3306
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3306
25.0%
9 3306
25.0%
7 3306
25.0%
0 3306
25.0%

Interactions

2023-10-03T02:52:33.836032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:20.878898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.042317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.173127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.338190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.465026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.774828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.877810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.042186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.245908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.359143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.484125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.934291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:20.990566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.138061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.271765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.433815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.557940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.868218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.974938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.146944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.339176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.456179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.579101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.026892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.084525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.228288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.366359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.523628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.651552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.961340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.071535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.253434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.428570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.546208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.668298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.126817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.186448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.327329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.466253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.620632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.753910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.054400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.175433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.346377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.524143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.644804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.767686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.220535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.282232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.418307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.562440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.712622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.843867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.143127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.270851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.455936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.613296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.736186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.088560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.312203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.372512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.514666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.663965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.802418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.930483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.230593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.370657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.550226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.708940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.825423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.178268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.404120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.464747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.608015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.756730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.892012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.016698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.318916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.466705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.647904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.800602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.914158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.267625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.501617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.562054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.702333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.856665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.986704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.307571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.413942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.563256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.747267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.894601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.009724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.364145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.604508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.664162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.807496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.952220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.095827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.404929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.511361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.665153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.851906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.986968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.114033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.467296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.696588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.755894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.895890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.046466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.185669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.499987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.602654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.758322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:29.940914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.077965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.205049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.558131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.789871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.848601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:22.985601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.142991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.276647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.590545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.692791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.851049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.040813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.169382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.298558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.650110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:34.886250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:21.944460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:23.077533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:24.239270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:25.368647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:26.681761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:27.784165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:28.945782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:30.144183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:31.262436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:32.391480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-03T02:52:33.740839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-10-03T02:52:38.938570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YearLife Expectancy World BankPrevelance of UndernourishmentCO2Health Expenditure %Education Expenditure %UnemploymentCorruptionSanitationInjuriesCommunicableNonCommunicableRegionIncomeGroup
Year1.0000.185-0.1330.0610.0950.031-0.050-0.0150.1190.004-0.0370.0450.0000.000
Life Expectancy World Bank0.1851.000-0.7800.5320.4320.3070.0750.2720.715-0.157-0.5810.0020.4000.578
Prevelance of Undernourishment-0.133-0.7801.000-0.502-0.451-0.379-0.185-0.319-0.7440.1320.534-0.0540.2780.462
CO20.0610.532-0.5021.0000.0330.070-0.001-0.1740.4650.6320.2580.7290.2950.097
Health Expenditure %0.0950.432-0.4510.0331.0000.4150.2790.0060.267-0.110-0.267-0.0320.2790.230
Education Expenditure %0.0310.307-0.3790.0700.4151.0000.1680.3980.264-0.201-0.331-0.1540.1400.206
Unemployment-0.0500.075-0.185-0.0010.2790.1681.000-0.078-0.008-0.170-0.215-0.1380.1960.230
Corruption-0.0150.272-0.319-0.1740.0060.398-0.0781.0000.309-0.333-0.305-0.2960.2500.299
Sanitation0.1190.715-0.7440.4650.2670.264-0.0080.3091.000-0.150-0.454-0.0270.3770.596
Injuries0.004-0.1570.1320.632-0.110-0.201-0.170-0.333-0.1501.0000.8650.9690.2060.103
Communicable-0.037-0.5810.5340.258-0.267-0.331-0.215-0.305-0.4540.8651.0000.7880.1880.122
NonCommunicable0.0450.002-0.0540.729-0.032-0.154-0.138-0.296-0.0270.9690.7881.0000.2830.125
Region0.0000.4000.2780.2950.2790.1400.1960.2500.3770.2060.1880.2831.0000.504
IncomeGroup0.0000.5780.4620.0970.2300.2060.2300.2990.5960.1030.1220.1250.5041.000

Missing values

2023-10-03T02:52:35.039979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-03T02:52:35.242666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-03T02:52:35.416783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country NameCountry CodeRegionIncomeGroupYearLife Expectancy World BankPrevelance of UndernourishmentCO2Health Expenditure %Education Expenditure %UnemploymentCorruptionSanitationInjuriesCommunicableNonCommunicableyear_dt
0AfghanistanAFGSouth_AsiaLow_income200156.30800047.8730.0NaNNaN10.809000NaNNaN2179727.109689193.705795426.381970
1AngolaAGOSub-Saharan_AfricaLower_middle_income200147.05900067.515960.04.483516NaN4.004000NaNNaN1392080.7111190210.532663516.341970
2AlbaniaALBEurope_&_Central_AsiaUpper_middle_income200174.2880004.93230.07.1395243.4587018.575001NaN40.520895117081.67140894.78532324.751970
3AndorraANDEurope_&_Central_AsiaHigh_income2001NaNNaN520.05.865939NaNNaNNaN21.7886601697.99695.5613636.641970
4United_Arab_EmiratesAREMiddle_East_&_North_AfricaHigh_income200174.5440002.897200.02.484370NaN2.493000NaNNaN144678.1465271.91481740.701970
5ArgentinaARGLatin_America_&_CaribbeanUpper_middle_income200173.7550003.0125260.08.3717984.8337417.320000NaN48.0539961397676.071507068.988070909.521970
6ArmeniaARMEurope_&_Central_AsiaUpper_middle_income200171.80000026.13600.04.6456272.4694410.912000NaN46.351896103371.75122238.13767916.191970
7American_SamoaASMEast_Asia_&_PacificUpper_middle_income2001NaNNaNNaNNaNNaNNaNNaNNaN1683.982933.9810752.131970
8Antigua_and_BarbudaATGLatin_America_&_CaribbeanHigh_income200174.171000NaN350.05.435876NaNNaNNaNNaN2201.123279.7214289.691970
9AustraliaAUSEast_Asia_&_PacificHigh_income200179.6341462.5345640.07.696229NaN6.740000NaN58.788894612233.81208282.734158052.861970
Country NameCountry CodeRegionIncomeGroupYearLife Expectancy World BankPrevelance of UndernourishmentCO2Health Expenditure %Education Expenditure %UnemploymentCorruptionSanitationInjuriesCommunicableNonCommunicableyear_dt
3296UkraineUKREurope_&_Central_AsiaLower_middle_income201971.8273172.61.747300e+057.0986025.441308.190000NaN71.9773382677198.111279508.7017610060.431970
3297UruguayURYLatin_America_&_CaribbeanHigh_income201977.9110002.56.490000e+039.3478324.703268.880000NaNNaN130120.4974036.98857057.521970
3298United_StatesUSANorth_AmericaHigh_income201978.7878052.54.817720e+0616.767063NaN3.670000NaN98.27522310129022.264968922.3195976524.311970
3299UzbekistanUZBEurope_&_Central_AsiaLower_middle_income201971.7250002.51.167100e+055.6176047.001865.8500002.5NaN1087494.201913988.837059738.931970
3300VietnamVNMEast_Asia_&_PacificLower_middle_income201975.4000006.23.364900e+055.2496564.061972.040000NaNNaN3100141.173281846.2819429515.981970
3301VanuatuVUTEast_Asia_&_PacificLower_middle_income201970.47400012.42.100000e+023.3603471.777881.8010003.0NaN12484.1826032.5669213.561970
3302SamoaWSMEast_Asia_&_PacificLower_middle_income201973.3210004.43.000000e+026.3630944.706258.4060004.047.6987886652.849095.1943798.621970
3303South_AfricaZAFSub-Saharan_AfricaUpper_middle_income201964.1310006.34.396400e+059.1093555.9177128.469999NaNNaN3174676.1013198944.7110214261.891970
3304ZambiaZMBSub-Saharan_AfricaLow_income201963.886000NaN6.800000e+035.3122034.4651812.5200002.5NaN510982.754837094.002649687.821970
3305ZimbabweZWESub-Saharan_AfricaLower_middle_income201961.490000NaN1.176000e+047.703565NaN4.8330002.525.963544644798.934187087.312364031.481970